Neural spline flow multi-constraint NURBS method for three-dimensional automatic geological modeling with multiple constraints

DOI10.1007/s10596-023-10202-9zbMath1520.86003MaRDI QIDQ6074253

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Publication date: 19 September 2023

Published in: Computational Geosciences (Search for Journal in Brave)

zbMATH Keywords

deep learning3D geological modelingautomatic modeling with multiple constraintsmulti-constraint NURBSneural spline flow

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Computational methods for problems pertaining to geophysics (86-08) General questions in geophysics (86A04)

Cites Work

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• Spline-based reservoir's geometry reconstruction and mesh generation for coupled flow and mechanics simulation
• Three-dimensional modelling of geological surfaces using generalized interpolation with radial basis functions
• Discrete smooth interpolation in geometric modelling
• Surface-based geological reservoir modelling using grid-free NURBS curves and surfaces
• Well-testing based turbidite lobes modeling using the ensemble smoother with multiple data assimilation
• Dynamic wettability alteration in naturally fractured rocks
• Comparison of the response to geometrical complexity of methods for unstationary simulations in discrete fracture networks with conforming, polygonal, and non-matching grids
• Model reduction for fractured porous media: a machine learning approach for identifying main flow pathways
• Scattered Data Interpolation: Tests of Some Method
• Geostatistics for Environmental Scientists
• Discrete modeling for natural objects

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